In [1124]:
ir.pl.clonotype_network(
adata_cohort_1_nt, color="patient_id", base_size=10, label_fontsize=9, panel_size=(7, 7)
)
Out[1124]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f013809bc40>
ir.pl.clonotype_network(
adata_cohort_1_nt, color="patient_id", base_size=10, label_fontsize=9, panel_size=(7, 7)
)
<matplotlib.axes._subplots.AxesSubplot at 0x7f013809bc40>
ir.pl.clonotype_network(
adata_cohort_1_nt, color="timepoint", base_size=10, label_fontsize=9, panel_size=(7, 7)
)
<matplotlib.axes._subplots.AxesSubplot at 0x7f0108378ee0>
ir.pl.clonotype_network(
adata_cohort_1_nt, color="cell_type", base_size=10, label_fontsize=9, panel_size=(7, 7)
)
<matplotlib.axes._subplots.AxesSubplot at 0x7f01081f8dc0>
ir.pl.clonotype_network(
adata_cohort_1_nt, color="expansion_on", base_size=10, label_fontsize=9, panel_size=(7, 7)
)
<matplotlib.axes._subplots.AxesSubplot at 0x7f0105caef70>
ir.pl.clonotype_network(
adata_cohort_1_nt, color="expansion", base_size=10, label_fontsize=9, panel_size=(7, 7)
)
<matplotlib.axes._subplots.AxesSubplot at 0x7f0105994ac0>
# VDJDB antigen annotation
ir.pl.clonotype_network(
adata_cohort_1_nt, color="VDJdb", base_size=10, label_fontsize=9, panel_size=(7, 7)
)
<matplotlib.axes._subplots.AxesSubplot at 0x7f01056ccd30>
# IEDB antigen annotation
ir.pl.clonotype_network(
adata_cohort_1_nt, color="IEDB", base_size=10, label_fontsize=9, panel_size=(7, 7)
)
<matplotlib.axes._subplots.AxesSubplot at 0x7f010539f610>
ir.pl.clonotype_network(
adata_cohort_1_nt, color="E2F4_score", base_size=10, label_fontsize=9, panel_size=(7, 7)
)
<matplotlib.axes._subplots.AxesSubplot at 0x7f01050639d0>
ir.pl.clonotype_network(
adata_cohort_1_nt, color="E2F4_activity", base_size=10, label_fontsize=9, panel_size=(7, 7)
)
<matplotlib.axes._subplots.AxesSubplot at 0x7f0104f89d30>
ir.pl.clonotype_network(
adata_cohort_2_nt, color="patient_id", base_size=10, label_fontsize=9, panel_size=(7, 7)
)
/home/ausserh/.local/lib/python3.8/site-packages/anndata/_core/anndata.py:1220: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Removing unused categories will always return a new Categorical object. c.reorder_categories(natsorted(c.categories), inplace=True) ... storing 'Patient_clonotype' as categorical /home/ausserh/.local/lib/python3.8/site-packages/anndata/_core/anndata.py:1220: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Removing unused categories will always return a new Categorical object. c.reorder_categories(natsorted(c.categories), inplace=True) ... storing 'expansion_on' as categorical
<matplotlib.axes._subplots.AxesSubplot at 0x7f0104c98730>
ir.pl.clonotype_network(
adata_cohort_2_nt, color="timepoint", base_size=10, label_fontsize=9, panel_size=(7, 7)
)
<matplotlib.axes._subplots.AxesSubplot at 0x7f0104a3b280>
ir.pl.clonotype_network(
adata_cohort_2_nt, color="expansion_on", base_size=10, label_fontsize=9, panel_size=(7, 7)
)
<matplotlib.axes._subplots.AxesSubplot at 0x7f01048015e0>
ir.pl.clonotype_network(
adata_cohort_2_nt, color="expansion", base_size=10, label_fontsize=9, panel_size=(7, 7)
)
<matplotlib.axes._subplots.AxesSubplot at 0x7f012e79a160>
# VDJDB antigen annotation
ir.pl.clonotype_network(
adata_cohort_2_nt, color="VDJdb", base_size=10, label_fontsize=9, panel_size=(7, 7)
)
<matplotlib.axes._subplots.AxesSubplot at 0x7f015a3503a0>
# IEDB antigen annotation
ir.pl.clonotype_network(
adata_cohort_2_nt, color="IEDB", base_size=10, label_fontsize=9, panel_size=(7, 7)
)
<matplotlib.axes._subplots.AxesSubplot at 0x7f01f7cf21c0>
ir.pl.clonotype_network(
adata_nt, color="patient_id", base_size=3, label_fontsize=9, panel_size=(7, 7)
)
/home/ausserh/.local/lib/python3.8/site-packages/anndata/_core/anndata.py:1220: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Removing unused categories will always return a new Categorical object. c.reorder_categories(natsorted(c.categories), inplace=True) ... storing 'Patient_clonotype' as categorical /home/ausserh/.local/lib/python3.8/site-packages/anndata/_core/anndata.py:1220: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Removing unused categories will always return a new Categorical object. c.reorder_categories(natsorted(c.categories), inplace=True) ... storing 'expansion_on' as categorical /home/ausserh/.local/lib/python3.8/site-packages/anndata/_core/anndata.py:1220: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Removing unused categories will always return a new Categorical object. c.reorder_categories(natsorted(c.categories), inplace=True) ... storing 'E2F4_activity' as categorical
<matplotlib.axes._subplots.AxesSubplot at 0x7f08f2fee9a0>
ir.pl.clonotype_network(
adata_nt, color="timepoint", base_size=3, label_fontsize=9, panel_size=(7, 7)
)
<matplotlib.axes._subplots.AxesSubplot at 0x7f08a0433610>
ir.pl.clonotype_network(
adata_nt, color="response", base_size=3, label_fontsize=9, panel_size=(7, 7)
)
<matplotlib.axes._subplots.AxesSubplot at 0x7f08a10e8d60>
ir.pl.clonotype_network(
adata_nt, color="cell_type", base_size=3, label_fontsize=9, panel_size=(7, 7)
)
<matplotlib.axes._subplots.AxesSubplot at 0x7f08a2000250>
ir.pl.clonotype_network(
adata_nt, color="expansion_on", base_size=3, label_fontsize=9, panel_size=(7, 7)
)
<matplotlib.axes._subplots.AxesSubplot at 0x7f08a10d0c70>
ir.pl.clonotype_network(
adata_nt, color="VDJdb", base_size=3, label_fontsize=9, panel_size=(7, 7)
)
<matplotlib.axes._subplots.AxesSubplot at 0x7f08a1cb8e80>
ir.pl.clonotype_network(
adata_nt, color="IEDB", base_size=3, label_fontsize=9, panel_size=(7, 7)
)
<matplotlib.axes._subplots.AxesSubplot at 0x7f089b8c5610>
ir.pl.clonotype_network(
adata_nt, color="E2F4_score", base_size=3, label_fontsize=9, panel_size=(7, 7)
)
<matplotlib.axes._subplots.AxesSubplot at 0x7f08c5959460>
ir.pl.clonotype_network(
adata_nt, color="E2F4_activity", base_size=3, label_fontsize=9, panel_size=(7, 7)
)
<matplotlib.axes._subplots.AxesSubplot at 0x7f08c4d391f0>
Test = adata_cohort_1_nt.obs["clone_id"].astype(str) + "_" + adata_cohort_1_nt.obs["patient_id"].astype(str)
Test_2 = Test.value_counts(normalize=False).to_frame()
Test = Test_2[~(Test_2[0] < 5)]
Test.head(20).style.bar()
| 0 | |
|---|---|
| 2249_BIOKEY_16 | 240 |
| 26688_BIOKEY_11 | 172 |
| 1_BIOKEY_10 | 160 |
| 26699_BIOKEY_11 | 139 |
| 26679_BIOKEY_11 | 110 |
| 14428_BIOKEY_12 | 97 |
| 14042_BIOKEY_12 | 95 |
| 4296_BIOKEY_14 | 93 |
| 2255_BIOKEY_16 | 83 |
| 12385_BIOKEY_5 | 82 |
| 14002_BIOKEY_12 | 74 |
| 16720_BIOKEY_1 | 72 |
| 13992_BIOKEY_12 | 65 |
| 7938_BIOKEY_28 | 63 |
| 7940_BIOKEY_28 | 61 |
| 12419_BIOKEY_5 | 56 |
| 21955_BIOKEY_4 | 53 |
| 16726_BIOKEY_1 | 50 |
| 14167_BIOKEY_12 | 49 |
| 25436_BIOKEY_18 | 47 |
count_arr = np.bincount(Test[0])
count_arr[5]
225
sum(Test.value_counts())
834
Test = adata_cohort_2_nt.obs["clone_id"].astype(str) + "_" + adata_cohort_2_nt.obs["patient_id"].astype(str)
Test_2 = Test.value_counts(normalize=False).to_frame()
Test = Test_2[~(Test_2[0] < 5)]
Test.head(20).style.bar()
| 0 | |
|---|---|
| 1640_BIOKEY_35 | 87 |
| 8_BIOKEY_33 | 87 |
| 6007_BIOKEY_34 | 74 |
| 1613_BIOKEY_35 | 70 |
| 2_BIOKEY_33 | 68 |
| 2224_BIOKEY_35 | 53 |
| 2946_BIOKEY_36 | 45 |
| 108_BIOKEY_33 | 41 |
| 7_BIOKEY_33 | 40 |
| 22_BIOKEY_33 | 39 |
| 1644_BIOKEY_35 | 35 |
| 4118_BIOKEY_41 | 34 |
| 86_BIOKEY_33 | 33 |
| 113_BIOKEY_33 | 30 |
| 4885_BIOKEY_32 | 30 |
| 118_BIOKEY_33 | 29 |
| 131_BIOKEY_33 | 29 |
| 148_BIOKEY_33 | 29 |
| 1633_BIOKEY_35 | 27 |
| 6_BIOKEY_33 | 27 |
count_arr = np.bincount(Test[0])
count_arr[5]
50
sum(Test.value_counts())
251
Test = adata_nt.obs["clone_id"].astype(str) + "_" + adata_nt.obs["patient_id"].astype(str)
Test_2 = Test.value_counts(normalize=False).to_frame()
Test = Test_2[~(Test_2[0] < 5)]
Test.head(20).style.bar()
| 0 | |
|---|---|
| 13873_P19 | 1656 |
| 13871_P19 | 1192 |
| 13872_P19 | 1124 |
| 13875_P19 | 503 |
| 6429_P2 | 312 |
| 32257_P38 | 311 |
| 13889_P19 | 305 |
| 11358_P14 | 237 |
| 6430_P2 | 202 |
| 9181_P13 | 187 |
| 13898_P19 | 171 |
| 13878_P19 | 167 |
| 13901_P19 | 163 |
| 13939_P19 | 154 |
| 13877_P19 | 147 |
| 13870_P19 | 145 |
| 27596_P35 | 125 |
| 13914_P19 | 124 |
| 17059_P25 | 116 |
| 13884_P19 | 116 |
count_arr = np.bincount(Test[0])
count_arr[5]
225
sum(Test.value_counts())
1601